MobileDMD-Net : Deformable MobileNet Backbone for Efficient Motion Deblurring

This paper introduces MobileDMD-Net, a lightweight deep neural network designed for real-time single-image motion deblurring in dynamic scenes. Unlike conventional approaches that rely on computationally intensive architectures or handcrafted priors, MobileDMD-Net employs a compact, encoder-decoder design optimized for efficiency without com- promising image restoration quality. The model integrates a blur-aware attention mechanism and frequency-guided feature fusion to effectively disentangle motion blur across varying spatial scales. Extensive experiments conducted on the Go- Pro and Köhler datasets demonstrate that MobileDMD-Net achieves state-of-the-art performance, with PSNR and SSIM improvements of up to 3.17 dB and 0.034, respectively, over strong baselines. Notably, the model requires only 4.10 GFLOPs and achieves an inference time of 0.014 seconds, enabling deployment on real-time systems. These characteristics make it well-suited for latency-sensitive applications such as onboard image enhancement for unmanned aerial vehicles (UAVs) and edge computing environments. The proposed method represents a compelling trade-off between accuracy and computational cost, advancing the practical feasibility of high-quality motion deblurring in real-world scenarios.